ReviewFrontiers in genetics2019
Inferring Interaction Networks From Multi-Omics Data.
Review in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed.
- Multi-omics network inference with a Gaussian copula model.BMC bioinformatics · 2026Article
- Assessing current capabilities for incorporating lipidomics in multiomics data integration.Briefings in bioinformatics · 2026Review
- Computational proteomics to enhance personalized treatment of COVID-19 and Long COVID.Clinical proteomics · 2026Review
- Fast autoregressive model for multivariate dependent outcomes with application to lipidomics analysis for Alzheimer's disease and APOE-Computational statistics & data analysis · 2026Article
- Network-based integrative analysis of multi-level regulatory mechanisms associated with glyphosate exposure.Frontiers in bioinformatics · 2026Article
- The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.Proteomics · 2025Review
- Network-based analyses of multiomics data in biomedicine.BioData mining · 2025Review
- Utilizing RNA-seq data in monotone iterative generalized linear model to elevate prior knowledge quality of the circRNA-miRNA-mRNA regulatory axis.BMC bioinformatics · 2025Article
- Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.Molecular systems biology · 2025Review
- ENQUIRE automatically reconstructs, expands, and drives enrichment analysis of gene and Mesh co-occurrence networks from context-specific biomedical literature.PLoS computational biology · 2025Article
- Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification.BioData mining · 2024Article
- Metabolite signatures of chronological age, aging, survival, and longevity.Cell reports · 2024Article
- AI-empowered perturbation proteomics for complex biological systems.Cell genomics · 2024Article
- Optimizing multi-omics data imputation with NMF and GAN synergy.Bioinformatics (Oxford, England) · 2024Article
- Smccnet 2.0: a comprehensive tool for multi-omics network inference with shiny visualization.BMC bioinformatics · 2024Article
- A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.Briefings in bioinformatics · 2024Review
- Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials.Nature biotechnology · 2024Article
- SmCCNet 2.0: A Comprehensive Tool for Multi-omics Network Inference with Shiny Visualization.bioRxiv : the preprint server for biology · 2024Article
- Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A major goal in systems biology is a comprehensive description of the entirety of all complex interactions between different types of biomolecules-also referred to as the interactome-and how these interactions give rise to higher, cellular and organism level functions or diseases. Numerous efforts have been undertaken to define such interactomes experimentally, for example yeast-two-hybrid based protein-protein interaction networks or ChIP-seq based protein-DNA interactions for individual proteins. To complement these direct measurements, genome-scale quantitative multi-omics data (transcriptomics, proteomics, metabolomics, etc.) enable researchers to predict novel functional interactions between molecular species. Moreover, these data allow to distinguish relevant functional from non-functional interactions in specific biological contexts. However, integration of multi-omics data is not straight forward due to their heterogeneity. Numerous methods for the inference of interaction networks from homogeneous functional data exist, but with the advent of large-scale paired multi-omics data a new class of methods for inferring comprehensive networks across different molecular species began to emerge. Here we review state-of-the-art techniques for inferring the topology of interaction networks from functional multi-omics data, encompassing graphical models with multiple node types and quantitative-trait-loci (QTL) based approaches. In addition, we will discuss Bayesian aspects of network inference, which allow for leveraging already established biological information such as known protein-protein or protein-DNA interactions, to guide the inference process.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.